{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: TkAgg\n"
     ]
    }
   ],
   "source": [
    "%matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.6, 1.2, 1.8, 2.4, 3. ])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import *\n",
    "x = linspace(0,3,6)\n",
    "x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.36, 1.44, 3.24, 5.76, 9.  ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = power(x,2)\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([3,5,7,6,2,6,10,15])\n",
    "plt.plot(x,y,'r')\n",
    "plt.plot(x,y,'g',lw=10)\n",
    "\n",
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "y = np.array([13,25,17,36,21,16,10,15])\n",
    "plt.bar(x,y,0.2,alpha=1,color='b')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as ny\n",
    "\n",
    "x = ny.linspace(-100, 100, 1000)\n",
    "y1 = pow(x,2)\n",
    "y2 = 2*x + 1\n",
    "plt.plot(x,y1)\n",
    "plt.figure(5)\n",
    "plt.plot(x,y2)\n",
    "plt.figure()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
